Prediction of vegetation pattern evolution in arid ecosystems using 3D-Var data assimilation

In arid regions, ecosystems are fragile, and vegetation exhibits high sensitivity to changes in climatic conditions. Vegetation patterns–non-uniform macroscopic structures formed by vegetation through temporal and spatial self-organization–serve as critical indicators of an ecosystem’s adaptive capacity, post-disturbance resilience, and early warning signals of ecosystem degradation. Investigating the formation mechanisms of vegetation patterns using reaction–diffusion (RD) models represents a vital approach to deciphering vegetation evolution dynamics, with significant implications for protecting arid ecosystems. However, heterogeneous steady-state solutions of RD systems, such as Turing patterns, often reside in multistable regions. This implies that minute variations in initial conditions may lead to markedly divergent outcomes. When initial vegetation distribution data are imprecise, predictions of vegetation evolution trends and steady-state distributions in a given spatial position using RD models become highly sensitive to initial errors—a case where “minor discrepancies in input yield vastly divergent results.” This study applies the three-dimensional variational data assimilation method to a RD model coupling vegetation, soil moisture, and surface water dynamics in arid regions. The results demonstrate that incorporating a modest amount of observational data can substantially enhance the model’s predictive accuracy for vegetation evolution trajectories.